Remote Sensing Applications-Society and Environment最新文献

{"title":"Using long-term bare earth composite image and machine learning in lithological mapping of Adrar Souttouf mafic complex (Oulad Dlim massif, Southern Morocco)","authors":"El Houcine El Haous ,&nbsp;Abdelkrim Bouasria ,&nbsp;Abdelilah Fekkak ,&nbsp;Faouziya Haissen ,&nbsp;Abdellatif Jouhari ,&nbsp;Ilyasse Berrada","doi":"10.1016/j.rsase.2025.101516","DOIUrl":"10.1016/j.rsase.2025.101516","url":null,"abstract":"<div><div>The success of geological mapping is mainly dependent on the best delineation of the lithological spatial features, among others. To this end, it is common to use remote sensing imagery supported with visual interpretation. The human visual perception is more capable of detecting and differentiating the colored compositions, however, it is not able to capture the information from the multispectral information. To overcome this issue, it is used to reduce the multidimensional information to three dimensions to be employed in colored visualizations. So far, the most tested methods are the linear ones (i.e. principal component analysis (PCA) and canonical correspondence analysis (CCA)) which were applied to a single date image. In this study, we explored innovative methods for lithological mapping to address the following questions: Can machine learning (ML) algorithms enhance the discrimination of key lithological features? Furthermore, can the identified patterns contribute to producing improved map that aids in resolving the two competing hypotheses—subduction or intracontinental rift—proposed for the Adrar Souttouf mafic complex in Moroccan Saharan domain. In this regard, we explored the potential of new ML methods and a composite image of the bare earth reflectance generated from Landsat-8/OLI image time series over ten years (from 2013 to 2023). We selected two new nonlinear methods which are Uniform Manifold Approximation and Projection (UMAP) and autoencoder (AE). The results of the visual interpretation were validated by an extensive field survey. The findings revealed that the linear methods (PCA and CCA) perform better in capturing the local details while the nonlinear methods (UMAP) were performant at the global patterns detection. Surprisingly, the AE was similar to PCA and CCA in local pattern discrimination. We also note that the nonlinear methods are powerful in capturing the whole information from the source data, contrary to the linear methods. These results could be suitable to serve as a basis for geological mapping in the studied massif. We also suggest that the developed methodology could be applied globally to other areas where the generation of barren land is possible.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101516"},"PeriodicalIF":3.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applications, challenges and perspectives for monitoring agricultural dynamics in the Brazilian savanna with multispectral remote sensing","authors":"Taya Cristo Parreiras ,&nbsp;Édson Luis Bolfe ,&nbsp;Paulo Roberto Mendes Pereira ,&nbsp;Abner Matheus de Souza ,&nbsp;Vinícius Fernandes Alves","doi":"10.1016/j.rsase.2025.101448","DOIUrl":"10.1016/j.rsase.2025.101448","url":null,"abstract":"<div><div>Land use and cover changes significantly impact landscape configuration, climate change, and society. The processes of expansion, conversion, intensification, diversification, and reduction materialize these changes in the agricultural environment. The Cerrado, or Brazilian Savanna, is a biodiversity hotspot, extremely important for water production, and one of the most important biomes for global food production. In this sense, monitoring agricultural dynamics in this environment plays a crucial role in sustainable planning, assessment of environmental impacts, and food security. In this study, we propose to analyze the evolution of the role of multispectral orbital remote sensing in mapping and monitoring agricultural dynamics processes in the Cerrado. Therefore, a narrative review of the literature based on studies developed in the biome was carried out to identify advances in tools, processes, and resources, as well as evaluate the challenges and perspectives for the future. Among other relevant results, monitoring these processes has become faster, more frequent, and more accurate, mainly through the combined use of high temporal resolution time series of spectral data and machine learning algorithms. Promising results have been obtained with Harmonized Landsat Sentinel-2 (HLS) data. The consolidation of deep neural networks has contributed substantially to detecting and delimitating complex intensification and diversification systems, such as central irrigation pivots and intercropping. However, there are challenges and obstacles to be faced, such as expanding the use of Sentinel-2 data, establishing means for sharing field data, and expanding studies to more fragmented landscapes, especially agricultural production on small properties.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101448"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143091897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Road extraction in diverse urban environments using UAV data and nDSM perturbations: A case of Bhopal, India","authors":"Ayush Dabra ,&nbsp;Vaibhav Kumar ,&nbsp;Jagannath Aryal","doi":"10.1016/j.rsase.2025.101465","DOIUrl":"10.1016/j.rsase.2025.101465","url":null,"abstract":"<div><div>Automated road extraction has a wide range of applications in urban planning, transportation management, and emergency response. However, existing methods struggle to extract roads in dense regions of developing countries, where the road networks are diverse and unplanned. This is due to the common spectral signatures between roads and neighboring objects, as well as the limited ability of current methods to combine multispectral and RGB images with normalized digital surface models (nDSMs). To address these challenges, we propose a novel approach that integrates UAV imagery from the Gehukheda region in Bhopal, India with high-resolution elevation data obtained from generated nDSMs and leveraging multispectral (RGB and NIR) and true-color RGB images to differentiate materials and elevation differences. We also introduce feature-aware strategic perturbations in the nDSM to improve segmentation efficiency. We trained three deep learning models, VGG19-UNet, DeepLabV3+, and SegFormer-B5 on our manually labeled training data. All three models performed well with the incorporation of nDSM and NIR. The perturbed DSM provided significantly better results, increasing the overall IoU of roads from 90.95% to 92.16% for VGG19-UNet, 90.59%–91.29% for DeepLabV3+, and from 91.75 to 93.68% for SegFormer-B5. These results demonstrate the effectiveness of our proposed approach in accurately segmenting roads, particularly within dense informal settlements. The proposed approach can help to overcome the limitations of satellite imagery and existing road extraction methods, thereby enhancing the accuracy and efficiency of road network identification and analysis in densely populated urban environments of developing countries.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101465"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143091901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A transfer learning-DCNN based oil spill detection using compact polarimetric SAR data","authors":"Mohammad Ebrahimi,&nbsp;Mahmod Reza Sahebi","doi":"10.1016/j.rsase.2024.101417","DOIUrl":"10.1016/j.rsase.2024.101417","url":null,"abstract":"<div><div>Oil spills in the marine environment can cause both economic and environmental crises, which underlines the urgent need for effective detection and prevention strategies to mitigate the consequences. Synthetic Aperture Radar (SAR) is one of the commonly used sensors for oil spill detection, and the compact polarimetric (CP) SAR system, with its wide swath width and sufficient polarimetric information, is well suited for this task. This study aims to employ four deep convolutional neural network (DCNN)-based semantic binary segmentation models (i.e., U-Net, LinkNet, FPN, and PSPNet) to detect oil spills using simulated compact polarimetry in hybrid-pol mode. In the deployed methodology, we used transfer learning to improve the adaptability of the models to different sensors. We efficiently adapted the built oil spill detection models on UAVSAR to data of RADARSAT-2 while retaining the essential features and knowledge from pre-trained models by fine-tuning them. The results showed the potential of these models in oil spill detection. The PSPNet model, as the most accurate, achieved an overall accuracy (OA) of 96.00% and a kappa coefficient of 91.30% on the UAVSAR image. After fine-tuning, it yielded an OA of 98.68% and a kappa coefficient of 92.71% on the RADARSAT-2 image.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101417"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Highly turbid and eutrophic small water bodies in West Africa well identified by a CNN U-Net algorithm","authors":"Mathilde de Fleury ,&nbsp;Manuela Grippa ,&nbsp;Martin Brandt ,&nbsp;Rasmus Fensholt ,&nbsp;Florian Reiner ,&nbsp;Gyula Maté Kovacs ,&nbsp;Laurent Kergoat","doi":"10.1016/j.rsase.2024.101412","DOIUrl":"10.1016/j.rsase.2024.101412","url":null,"abstract":"<div><div>Although high-resolution multispectral optical imagery is increasingly being used to monitor continental surface waters more easily than ever before, there are still limitations to the methods used to extract water bodies. Detecting water becomes particularly difficult in the presence of aquatic vegetation or trees, or when spectral variations across the water surface are high. These limitations pose significant challenges in West Africa, where such cases are numerous, hindering the application of widely used methods and leading to a reduced quality of various existing datasets. As a result, the region lacks comprehensive information on the number of water bodies, their surface area, their spatial distribution and their typology. In this study, we propose a method based on a convolutional neural network based on a U-net architecture, which we apply to images from the Sentinel-2 multispectral instrument acquired in November 2020 and March 2018, corresponding to the maximum and minimum water area extent during the 2016–2020 period. We observe a much larger number of lakes than in current datasets, a large proportion of which are small and temporary. Overall, 29,265 water bodies were classified in November 2020 and 8,093 in March 2018 over an area of 1,340,450 km<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> in the central Sahel, with sizes ranging from 0.002 km<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> to 1,162 km<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>. In addition, a wide diversity of optical water types was found across the water bodies: hypereutrophic water bodies dominate, accounting for 67.9% in November 2020, followed by very turbid water bodies representing 26.1%. The Convolutional Neural Network U-Net algorithm successfully identified water bodies with aquatic vegetation or obscured by trees, as well as extremely turbid small lakes and reservoirs, which are often missing in global datasets. Such improved mapping capability has important implications for the monitoring of water resources and water quality, which are pivotal for the livelihoods of the region.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101412"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seasonal variations and trends in solar UV spectral irradiances based on data from the Ozone Monitoring Instrument at solar noon in Southern Amazonas, Brazil","authors":"Péricles Vale Alves ,&nbsp;Vandoir Bourscheidt ,&nbsp;Luiz Octávio Fabrício dos Santos ,&nbsp;Paula Regina Humbelino de Melo","doi":"10.1016/j.rsase.2024.101423","DOIUrl":"10.1016/j.rsase.2024.101423","url":null,"abstract":"<div><div>Ultraviolet (UV) radiation has significant implications for public health and the environment, making it crucial to understand the dynamics of UV irradiances, particularly in sensitive regions such as the southern mesoregion of Amazonas. This study aimed to analyze the seasonal variations and trends in UV irradiances (305, 310, 324, and 380 nm) in the mentioned region using remote sensing data. The data were derived from satellite-mounted sensors, covering the period from January 2005 to December 2022. The results indicate a well-defined seasonality of UV irradiances, with intensity peaks in summer and spring. The largest and smallest monthly variations in UV irradiances (305 and 310 nm) occurred in February and September, respectively, while for UV irradiances (324 and 380 nm), these variations were observed in November and September. As for the trends, the most significant findings included substantial increases in UV irradiances (324 and 380 nm) and a reduction in Cloud Optical Thickness (COT). A significant negative correlation between ozone and UV irradiance (305 nm) was also observed, along with a strong correlation between COT and UV irradiances (324 and 380 nm). The study revealed a critical situation in July, emphasizing the need for additional precautions regarding UV exposure. While the results indicate concerning behaviors in irradiances and COT, the lack of spectral UV sensors on the ground in the southern Amazon region highlights the urgent need for investment in advanced monitoring technologies so that further studies can describe these dynamics more precisely.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101423"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements in UAV remote sensing for agricultural yield estimation: A systematic comprehensive review of platforms, sensors, and data analytics","authors":"Shubham Anil Gade ,&nbsp;Mallappa Jadiyappa Madolli ,&nbsp;Pedro García‐Caparrós ,&nbsp;Hayat Ullah ,&nbsp;Suriyan Cha-um ,&nbsp;Avishek Datta ,&nbsp;Sushil Kumar Himanshu","doi":"10.1016/j.rsase.2024.101418","DOIUrl":"10.1016/j.rsase.2024.101418","url":null,"abstract":"<div><div>Traditional yield estimation approaches are quite tedious, time-consuming, and labor-intensive. Unmanned aerial vehicles (UAVs) present an exciting opportunity to estimate crop yield with high spatial and temporal resolution in agriculture. The objective of this article is to review current studies and research works in agriculture that employ the use of different UAV platforms, sensors, data acquisition, machine learning and photogrammetry techniques, and vegetation indices in UAV-based crop yield prediction. Furthermore, the article also explores the challenges and limitations in yield estimation. Hundred different studies from Google Scholar, Scopus, and Web of Science are presented and reviewed. The result demonstrated that most of the studies are centered on China and USA. Supervised learning models are widely used and exhibit better accuracy in yield estimation. The normalized difference vegetation index (NDVI) is preferred by researchers and emerges as a widely used vegetation index (60 studies). The study concluded that UAV-based crop remote sensing can be an effective method for improving yield estimation. The integration of multimodal data, including textural, structural, thermal, and meteorological features, along with key spectral bands such as near-infrared (NIR) and red-edge (RE), has demonstrated potential for improving the accuracy of yield estimation models. Moreover, supervised models have shown great suitability for cereal crops. Random Forest and linear regression emerge as reliable options for estimating yields of major crops, such as wheat, rice, and maize. However, challenges in yield estimation with UAV-based remote sensing include regulatory constraints, weather conditions, data storage and management, high initial costs, and technical limitations.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101418"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applications based on EGMS products: A review","authors":"M. Crosetto ,&nbsp;B. Crippa ,&nbsp;M. Mróz ,&nbsp;M. Cuevas-González ,&nbsp;S. Shahbazi","doi":"10.1016/j.rsase.2025.101452","DOIUrl":"10.1016/j.rsase.2025.101452","url":null,"abstract":"<div><div>The European Ground Motion Service (EGMS) represents the largest wide area Persistent Scatterer Interferometry service ever conceived. It is part of the Copernicus Land Monitoring Service's product portfolio. Thanks to its technical characteristics, and the fact that EGMS products are made available on a full, open, and free-access principle, the EGMS has the potential to become a game changer in the way ground motion data are used in Europe. Three years after the publication of the first EGMS products, this initial review studies the scope and impact of this service in terms of applications. After a brief introduction to the service, the paper describes the main EGMS trends, including the procedures to analyse and exploit its data, and a review of its main applications. The quantity and quality of the applications are useful ways to show the potential of the service. This can open the door to a future widespread use of EGMS data. Next, the paper features a technical discussion on the main characteristics of the EGMS products, the main EGMS validation activities, and future research lines.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101452"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Error-reduced digital elevation models and high-resolution land cover roughness in mapping tsunami exposure for low elevation coastal zones","authors":"Rajuli Amra ,&nbsp;Susumu Araki ,&nbsp;Christian Geiß ,&nbsp;Gareth Davies","doi":"10.1016/j.rsase.2024.101438","DOIUrl":"10.1016/j.rsase.2024.101438","url":null,"abstract":"<div><div>This study presents a systematic exposure assessment by reconstructing the impact of the 2004 Indian Ocean Tsunami using a wide range of inundation scenarios and multiresolution exposure layers. To develop inundation and exposure models, we employed the error-reduced global digital elevation models (DEMs) and geospatially consistent multiresolution datasets: land cover roughness (LCR) models, built-up areas, and gridded population layers. We implemented three sequential validation assessments to evaluate the performance of inundation models, incorporating satellite observations, post-tsunami measurements, and the confidence level associated with inherent DEM error characteristics. The results demonstrated that the error-reduced variants of Copernicus DEM (i.e., FABDEM and DiluviumDEM) satisfied all reliability criteria. Incorporating these elevation models with LCR models improved the accuracy of inundation depth estimates; however, it reduced the agreement between simulated and observed inundation extents. We observed that applying high-resolution LCR models had a minimal impact on overland inundation extents but still influenced the exposure assessment, especially in high-density urban areas.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101438"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Satellite-derived shallow water depths estimation using remote sensing and artificial intelligence models, a case study: Darbandikhan Lake Upper, Kurdistan Region, Iraq","authors":"Arsalan Ahmed Othman ,&nbsp;Salahalddin S. Ali ,&nbsp;Ahmed K. Obaid ,&nbsp;Sarkawt G. Salar ,&nbsp;Omeed Al-Kakey ,&nbsp;Younus I. Al-Saady ,&nbsp;Sarmad Dashti Latif ,&nbsp;Veraldo Liesenberg ,&nbsp;Silvio Luís Rafaeli Neto ,&nbsp;Fabio Marcelo Breunig ,&nbsp;Syed E. Hasan","doi":"10.1016/j.rsase.2024.101432","DOIUrl":"10.1016/j.rsase.2024.101432","url":null,"abstract":"<div><div>Bathymetric mapping provides valuable information for the estimation of the depth and volume of enclosed inland water bodies that are useful in the planning and management of water resources. The use of conventional methods for the detection of shallow water depth, specifically in flooded areas, has been challenging. However, advances in remote sensing technology combined with artificial intelligence (AI) offer a reliable method. This study presents a reliable method to estimate water depth, using the Darbandikhan Lake Upper (DLU) as a test site. The novelty of this work lies in using a combination of Quantile Regression Forests (QRF), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANN) approaches together with the reflectance of Sentinel-2 and the ICESat-2 LiDAR data to estimate the depth of the water in the DLU during the 2019 spring flood. Our results gave the coefficient of determination (R²) and root mean square error (RMSE) between the actual depth obtained from the ICESat-2 and the estimated depth from the applied artificial intelligence models of 0.984, 0.983, 0.868, and 0.809; and 0.545, 0.569, 1.618, and 2.143 for the QRF, RF, SVM, and ANN models, respectively. This study, which applied the QRF model for the first time to determine the satellite-derived water depths, produced the most accurate result, with the maximum and mean estimated depth of DLU being 19.93 and 6.29 m, respectively. This study shows that the most sensitive bands to estimate the bathymetry are Band 9 (940 nm), Band 3 (560 nm), and Band 5 (705 nm) of the Sentinel-2, while the less sensitive bands are Band 2 (490 nm) and Band 11 (1610 nm). We argue that this technique can be applied to estimate the depth of shallow water bodies using passive satellite imageries in other regions of the world regardless of the full coverage availability of ICESat-2.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101432"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}